2069 results about "Data breach" patented technology

The invention relates to the technical field of railway data security, and provides a railway data asset security monitoring and risk early warning method and system, and the method comprises the steps: collecting multi-dimensional feature data of railway data assets, including equipment operation state data, network communication data, data access logs and environment perception data; performing heterogeneous data fusion preprocessing on the multi-dimensional feature data to generate a standardized feature vector; calculating security evaluation indexes of the standardized feature vector based on a dynamic weight distribution algorithm, wherein the security evaluation indexes comprise an integrity evaluation value, an availability evaluation value and a confidentiality evaluation value; generating a multi-level risk early warning signal according to the deviation degree of the safety evaluation index and the dynamic threshold value; and triggering dynamic control of a data asset protection strategy based on the risk early warning signal, wherein the dynamic control comprises access permission adjustment, data encryption strength grading and flow limitation. According to the invention, the safety and reliability of railway data assets can be improved, and data leakage and system fault risks are reduced.

The invention discloses a distributed storage method and system for data security, and belongs to the technical field of data storage security, the method comprises the following steps: firstly, fragmenting original data, and generating a unique identifier for each data fragment; for each fragment, an encryption algorithm and key credentials are adaptively selected, and the key credentials are stored in a key management system. By performing homomorphic encryption on the key voucher, an encryption handle is generated, and efficient positioning and retrieval of the key are realized. According to the method, a differentiated encryption strategy is adopted, a most suitable encryption scheme is selected according to fragment characteristics, and encrypted data fragments are dispersed and stored in a plurality of distributed storage nodes. A global mapping index is constructed in a metadata management server, and a corresponding relation between an original data global identifier and metadata of each data fragment is recorded, including information such as identifiers before and after encryption, a storage position, an encryption algorithm, a key handle and the like. According to the method, the data leakage risk is effectively reduced, and the safety and reliability of the storage system are improved.

The invention discloses a dynamic authority management system and method based on multi-source salary data integration, and relates to the technical field of salary data management, and the method comprises the following steps: collecting role names, authority boundaries and operation granularity information from a plurality of business systems, and generating structured role semantic ontology entries based on a field-level semantic annotation mode; and disassembling the to-be-mapped role according to the authority dimension, calculating a semantic similarity index between the to-be-mapped role and the standard role based on the role semantic ontology library, and outputting a role consistency scoring matrix. According to the method, the authority difference is identified through the role semantic ontology and the similarity score, the minimum authorization judgment is realized in combination with the rule engine and reinforcement learning, the audit traceability and anomaly detection are guaranteed by using the block chain and the dynamic graph, and finally a strategy self-evolution closed loop is constructed. And the problems of permission mismatching and data leakage caused by role semantic inconsistency are effectively solved.

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

The invention discloses a data security management method and system based on cloud computing, and relates to the technical field of data management, and the method comprises the following steps: S1, making a security policy; s2, performing security assessment on the cloud infrastructure; s3, performing identity authentication and access control; s4, data encryption and key management; s5, data classification and level-to-level management; s6, data leakage detection and defense; s7, performing auditing and compliance management; s8, abnormal behavior monitoring and threat response; and S9, an elastic backup and recovery mechanism. According to the data security governance method and system based on cloud computing, the elasticity and dynamic response capability of data security are remarkably improved by setting the distributed key management system and the intelligent monitoring model. The distributed key management system dispersedly stores the keys in different physical areas, so that the risk of single-point fault and attack is reduced; the AI-based intelligent behavior monitoring system analyzes user behaviors in real time, predicts abnormal operation and automatically responds.

The invention discloses a mobile collaboration platform data management method, and relates to the technical field of data processing, and the management method comprises the following specific steps: S100, data sensitive grading: calculating sensitivity scores of various types of data in a mobile collaboration platform by using a formula. According to the method, the sensitivity scores of various data are calculated through a formula, and the data are divided according to the sensitivity levels, so that different levels of protection measures are taken for the data with different sensitivity degrees, and meanwhile, through a built-in sensor and a built-in behavior monitoring module, the sensitivity of the data is improved. The operation behavior information, the equipment information and the business process information of the user are collected in real time, the current business scene is intelligently judged, a basis is provided for subsequent privacy protection strategy making, the data processing efficiency is improved, the data security is greatly enhanced, and data leakage and abuse are effectively prevented.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention relates to the technical field of information security, in particular to a cloud-based EMM encryption method and system, and the method comprises the following steps: collecting user login data and equipment use data based on mobile equipment operation information, recognizing the time and position information of user login and equipment use, calculating the risk scores of various behavior modes, and storing the risk scores of various behavior modes. And obtaining user behavior identification data. According to the method, the comprehensiveness and the dynamism of data security management are improved by collecting and analyzing the user login data and the equipment use data in real time, identifying unauthorized access and performing risk scoring on the mobile equipment, and encryption parameters are optimized by combining real-time monitoring on the user behavior pattern and the equipment performance and utilizing data sensitivity scoring; the data encryption efficiency and security are improved, the risk of data leakage is reduced by adjusting the updating frequency and parameters of the encryption key, the response speed to security threats is improved, the flexibility of enterprise data protection is enhanced, and the service continuity is maintained.

The present application relates to the field of e-government technology, and discloses an e-government privacy and security protection system based on blockchain technology, including a blockchain layer, a data storage layer, a smart contract layer, a user layer, a privacy computing module, a dynamic authorization and trust scoring module, a revocable access permission module, a data tampering detection and reporting module, a decentralized identity management module, and an on-chain audit module; the blockchain layer is used to record and store data and transactions generated in the e-government process; the data storage layer is used to store encrypted sensitive data, and store the corresponding hash value and index on the blockchain. The computing task allocation is dynamically adjusted through the automatic coordination mechanism of the smart contract to ensure computing efficiency and data privacy. At the same time, the on-chain audit tool immediately verifies the compliance of the results and generates reliable decision-making recommendations, thereby providing reliable decision support and avoiding data leakage risks.

The invention relates to the technical field of data processing and privacy protection, in particular to a data desensitization and integrity verification method and system based on a differential privacy algorithm, and aims to realize collaborative optimization of data privacy protection and integrity guarantee. Determining a total privacy budget by calculating global sensitivity, a data leakage risk coefficient and independent and joint data privacy information amount, dynamically allocating the privacy budget for a single-task or multi-task scene, and injecting noise based on an allocation result to realize differential privacy desensitization; meanwhile, a verification voucher is generated by using a hash function, a digital signature and an alliance chain, and the data integrity is ensured through signature verification, data consistency verification and block chain verification. The system comprises a data application processing module, a global sensitivity calculation module, a desensitization core processing module, a verification voucher generation module and an integrity verification module, is suitable for government affairs, enterprises and other scenes with high requirements for data privacy and integrity, and effectively improves the safety and reliability of data processing.

The invention discloses an enterprise registration archive sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing, and relates to the technical field of archive management. The system comprises an identity authentication module, an archive storage management module, an archive sharing module, a shareholder right of knowledge guarantee module, a comprehensive statistics module and a system security module. According to the system, full-chain tracing of archive data is realized by utilizing a block chain technology, all change operations are completely recorded, and the transparency of archive management is remarkably improved; the security and authenticity of the shared archive are ensured through electronic signature, watermark and two-dimensional code anti-counterfeiting technologies; shareholders can track stock equity states and change records of enterprises in real time and know enterprise change information in time through a dynamic notification mechanism, so that the right of knowing of the shareholders is comprehensively guaranteed; through dynamic encryption, authority management and abnormal behavior detection, the security of file storage and access is ensured, and data leakage and tampering are effectively prevented.

The invention relates to the technical field of network security and data protection, and discloses a data leakage prevention method and system based on user behavior perception, and the method comprises the steps: obtaining historical operation data, and constructing a personalized behavior reference library; monitoring a current access behavior in real time by sliding a time window, calculating a deviation degree and triggering anomaly detection; performing multi-level feature analysis on the abnormal behavior and calculating a comprehensive abnormal score; dynamically adjusting the access authority according to the score, recording an abnormal behavior and carrying out relevance matching; optimizing the reference model through feedback learning; and evaluating the credibility and the risk level based on an accurate modeling result, and adaptively adjusting permission configuration. According to the method, the user behavior rule can be accurately captured, the data leakage risk can be efficiently identified, the access permission can be dynamically adjusted, the false alarm rate can be reduced, adaptive protection can be realized, and data security and business smoothness can be guaranteed.

The invention discloses a market supervision data asset management method and system based on compliance and privacy protection, and relates to the technical field of data asset management. According to the method, the privacy compliance strategy is generated according to the metadata label and is packaged into the data capsule, so that the binding of the data and the compliance strategy is realized; the privacy compliance knowledge graph is constructed, and semantic matching and compliance verification are performed in combination with a large language model, so that the semantic consistency and matching degree between a privacy compliance strategy and a supervision regulation are improved, and manual intervention is reduced; a privacy protection processing mechanism is called according to a compliance verification result, and a proper data protection technology is dynamically applied to reduce the risk of data leakage; anonymization processing parameters are optimized before data are published, data distortion processing is implemented, and the privacy disclosure risk is effectively controlled on the premise that data availability is guaranteed; and a data asset value dynamic evaluation model is constructed, the data comprehensive value and the risk level are evaluated, and refined and differentiated management of data assets is supported.

The invention discloses an administrative institution internal control information processing method and system based on big data analysis, and relates to the technical field of big data information processing. And furthermore, by capturing dynamic association characteristics generated by interaction of local contents in the business field and the information, internal control information cross-dimension semantic joint coding representation with context sensing capability is generated, and cross-dimension semantic interaction analysis between the business field and information content semantics is further realized by applying a chain reasoning model. The system can automatically identify potential logical association between the business field and the information content, so that the risk of data leakage can be greatly reduced, and the scientificity of desensitization processing can be improved, thereby enhancing the effectiveness and efficiency of internal control of administrative institutions.

The invention relates to the technical field of system security, in particular to a computer storage file protection system which comprises a risk analysis module, a dynamic encryption module, an authentication management module and a behavior monitoring module. According to the method, the response speed and the personalized protection level of the file security are improved by automatically collecting location, time and user information parameters and executing context risk logic analysis, the system can provide customized protection schemes for different security requirements by dynamically adjusting an encryption mechanism and the key length, and the security of the file is improved. The system is allowed to strengthen protection measures under the high-risk condition, resource use is optimized under the low-risk condition, unnecessary performance burdens are avoided, through real-time monitoring and behavior analysis, the system can recognize and respond to abnormal behaviors in time, unauthorized access and data leakage are effectively prevented, potential threats to sensitive information are reduced, and the safety of the system is improved. The overall security management capability of the system is improved, and the continuity and effectiveness of data protection are ensured.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention discloses a financial privacy security alignment method and system based on federated learning and adversarial training, and the method comprises the steps: enabling a plurality of financial institution clients to obtain local financial data, and carrying out the semantic analysis and sensitivity grading; constructing a federated learning framework, performing homomorphic encryption on a local model gradient by adopting Paillier, uploading the local model gradient to an aggregation server for secure aggregation, and generating global gradient update parameters; dynamically selecting a desensitization strategy based on the business scene weight and the data sensitivity, and executing field-level desensitization processing on the financial data; an adversarial sample is injected in local model training, and a task loss function and an adversarial loss function are jointly optimized; and mapping compliance terms into technical rules, and completing privacy protection and compliance alignment. According to the method, data privacy protection is enhanced, the data leakage risk is effectively reduced, and financial data privacy security is ensured. Safe transmission and effective aggregation of data are ensured, and the global model training efficiency and safety are improved. An adversarial sample is injected, and high accuracy and stability are kept.

The invention provides a credible sharing method and system for multi-party industrial data, and relates to the technical field of data processing, and the method comprises the steps that multiple participants encrypt local industrial data through a credible execution space, and generate encrypted data and corresponding data fingerprints; defining a data access rule based on the smart contract; verifying and auditing the data access request, and obtaining an encrypted data authorization certificate after verification and auditing; and under the federated learning framework, the trusted execution space of each data provider executes the privacy calculation task based on the authorization certificate, and outputs a task calculation result and sends the task calculation result to the task requester. According to the method and the device, the technical problem of privacy risk of data leakage or tampering caused by contradiction between multi-party industrial data sharing and privacy protection is solved, the privacy can still be ensured while data sharing is ensured through federated learning and trusted execution space, and the security and the credibility of data sharing are improved.

The invention relates to a health data encryption storage method and device, equipment and a storage medium, and the method comprises the steps: obtaining to-be-processed health data, carrying out the sensitivity analysis and grade division of the health data, and obtaining health data sub-blocks; performing encryption preprocessing on the health data sub-blocks, and performing group data protection according to a preset differential privacy algorithm to obtain encrypted data blocks; generating an initial master key through a hardware security module, and performing derived hierarchical encryption on the encrypted data block to obtain a data encryption key; performing fragmentation processing and regular distributed storage on the data encryption key to obtain a dynamic security key; and performing metadata separation storage on the encrypted data according to the dynamic security key, and performing data permission determination according to a preset role-based access control mechanism to obtain an encrypted isolation database. According to the invention, efficient security protection can be maintained under different application scenes and access permissions, and the risk of data leakage is reduced.

The invention relates to a network data leakage monitoring system and method, and the system comprises a data collection module, a data preprocessing module, an anomaly detection module, a risk assessment module and an alarm module. The data acquisition module is used for acquiring network data from a network data source in real time; the data preprocessing module is used for cleaning and standardizing the acquired network data to obtain preprocessed network data; the anomaly detection module identifies an anomaly mode and calculates a confidence score according to the preprocessed network data by using a pre-trained neural network model; the risk assessment module assesses the potential data leakage risk in real time based on the type of the abnormal mode and the confidence score, and generates a comprehensive risk assessment result; and the alarm module generates and sends alarm information when the comprehensive risk assessment result exceeds a preset threshold value. The system can accurately detect and evaluate the network data leakage risk in real time, and provides effective security alarm.

The invention discloses a charging facility data security protection method and system, electronic equipment and a storage medium, and relates to the technical field of information security. The method comprises the following steps: verifying the identity of a vehicle based on a charging request to obtain a verification result; when the verification result is successful, obtaining a charging parameter of a control layer of the target charging pile and a billing parameter of a service layer based on the charging request; generating a first key of a control layer and a second key of a service layer based on the vehicle information of the vehicle and the charging request; performing first encryption processing on the charging parameter based on the first key to obtain first encrypted data, and performing second encryption processing on the charging parameter based on the second key to obtain second encrypted data; and the first encrypted data and the second encrypted data are transmitted to a vehicle terminal of the vehicle after time sequence identifiers are added to the first encrypted data and the second encrypted data. By implementing the technical scheme provided by the invention, the confidentiality of the charging parameter and the billing parameter can be improved, the risks of data leakage, tampering and the like are reduced, and the data security of the charging station is further improved.

The invention discloses a maritime accident prediction method and device based on interpretable integrated machine learning, and relates to the technical field of maritime affair safety risk analysis, and the method comprises the steps: obtaining accident investigation data, carrying out the preprocessing, balancing the data through a ten-fold layered oversampling method, and carrying out the cross verification training, and determining a performance optimal model by using the test set and carrying out interpretable analysis to explain the influence of the characteristics on the accident prediction result. By constructing a closed-loop'data processing-model optimization-explanation output 'process and adopting SMOTE oversampling and ten-fold layered cross validation training and a heterogeneous base model ensemble learning strategy, the processing capacity of the data imbalance problem of accident categories is improved, the data leakage problem of oversampling is avoided, and the possible bias of a single model is overcome. The interpretability analysis of the model prediction result can quantitatively display the contribution degree of each feature to prediction globally and locally, reveal the nonlinear relationship and interaction effect between the features, and provide transparent interpretation of model decision.

The invention relates to the technical field of data security, in particular to a big data security storage method and system based on cloud computing, and the method comprises the following steps: initializing a cloud service node, selecting data blocks needing security storage, distributing an identifier for each data block, sorting and classifying the data blocks by adopting a batch processing mode, and storing the sorted data blocks into a database; and obtaining an initialized data block. According to the method, the security of the data in the cloud environment is greatly improved through key segmentation and storage of part of keys in a plurality of security cloud service nodes, the decryption rule is constructed by using the threshold cryptography model, the data can be decrypted only when a sufficient number of key parts are collected, unauthorized access and data leakage are effectively prevented, and the security of the data in the cloud environment is improved. The strategy of dynamically adjusting the secret key replacement period adapts to the access frequency and the change dynamic state of the storage data block, and the confidentiality, the integrity and the availability of the data are improved, especially in a sensitive service scene with a large data volume.

The invention belongs to the technical field of communication, and discloses a network transmission management method and system, and the method comprises the steps that a data collection module collects multi-dimensional data in real time; the decision planning module optimizes a network route; the decision planning module performs protocol and encryption switching; the execution module dynamically adjusts the transmission strategy; the safety protection module performs four-dimensional dynamic protection on the data; the hybrid cloud deployment module guarantees the confidentiality of core data; the multi-active data center module realizes data synchronization among a plurality of data centers; the cross-domain collaborative module realizes privacy protection collaborative training between edge nodes through federated learning; and the block chain auditing traceability module records the key operation log. According to the method, routing and flow prediction are optimized through a mixed quantum classical calculation engine, multi-protocol heterogeneous scheduling and neural mimicry congestion control are combined, the network transmission efficiency is greatly improved, the data transmission speed is improved, and the data leakage risk is reduced through application of a zero-trust security matrix and a quantum key distribution technology.

The invention discloses an automatic input vital sign detection method and system, and relates to the technical field of medical information processing, and the method comprises the steps: collecting vital sign data of a target individual, and carrying out the synchronous processing; a vital sign data set is formed through cleaning and multi-channel data fusion; uploading the encrypted data to a medical information system to finish classified storage and updating of the data; performing feature extraction and analysis to generate a health risk prediction result; health management suggestions are generated, and data display is completed through a dynamic feedback mechanism. According to the method, the privacy and the integrity of the vital sign data are guaranteed through a dynamic updating and verification mechanism in a mode of combining symmetric encryption and asymmetric encryption, and the risks of data leakage, data tampering and version management disorder in a traditional system are overcome. By introducing an intelligent analysis method combining a classification model and a regression model, the limitation of traditional single-dimensional static assessment is solved, and a comprehensive health risk report is generated by integrating logic association among multi-modal feature data.

The invention discloses a container and encryption-based data behavior security management method. The method comprises the steps of preparation, data asset creation and association, data storage, data asset behavior recording, data asset behavior management and control and data asset output. The data is encrypted and stored by adopting an encryption technology, and the original data is converted into the ciphertext, so that the data is prevented from being illegally acquired in the transmission and storage process; access authority is strictly limited through RBAC control, it is ensured that only authorized users or application programs can access and operate data, and data leakage and tampering are prevented; data injection is realized by utilizing a data volume of a containerized platform, so that an application program in a container accesses external data, the data can be shared in different environments conveniently, the data can be stored persistently, and the availability of the data is ensured; the containerization expandability is convenient for expansion and adjustment when the data scale is expanded; the binding of the data ID and the comprehensive behavior record flow ensure the unique identification of the data and improve the accuracy and traceability of the data.

The invention discloses a mixed strategy Text-to-SQL method and device, electronic equipment and a storage medium, and relates to the technical field of natural language processing and database query, the method comprises the following steps: generating question and answer pairs based on a large language model, and constructing a vector database based on the question and answer pairs and schema information of a target database; in the vector database, performing complementary mask retrieval on the sample questions to obtain schema information to be used and question and answer pair examples; and analyzing the sample SQL statement to obtain target sc hema information. Constructing a first prompt template based on the sample problem and to-be-used schema information so as to perform fine adjustment on the data table recall model; constructing a second prompt template based on the sample question, the question and answer pair example and the target schema information so as to perform fine adjustment on the Text-to-SQL model; and based on the fine-tuned data table recall model and the Text-to-SQL model, reasoning an SQL statement of the problem to be processed. The problems of data leakage and dependence on training data are solved, and the Text-to-SQL performance is improved under the condition that the number of tokens is limited.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.